Adaptive Management of Microservices in Dynamic Computing Environments: A Taxonomy and Future Directions

2026-04-28

AI Summary

This study addresses the challenges microservices face in dynamic environments—such as load fluctuations, network variations, and failures—which hinder the coordination of scaling, routing, and repair strategies. The work presents the first taxonomy for adaptive microservice management tailored to dynamic settings, systematically reviewing 84 systems and 13 evaluation artifacts across four dimensions: control placement, dynamic modeling, adaptation strategies, and evaluation evidence. It identifies critical limitations in existing approaches, particularly incomplete modeling of dynamics and insufficient evaluation fidelity, underscoring the importance of high-fidelity evaluation for realizing performance gains. The paper further outlines promising future directions, including cross-layer coordination, telemetry-driven control abstractions, and safe learning-based control, offering a structured roadmap for subsequent research.

Abstract

Microservice-based cloud applications face changing workloads, evolving request paths, variable network conditions, interference, and failures. These dynamics couple autoscaling, placement, routing, isolation, and remediation. The survey examines dynamics-aware adaptive management for microservices. Its taxonomy covers control locus, modeled dynamics, adaptation strategy, and evaluation evidence; objectives and telemetry are cross-cutting. A synthesis of 84 system entries and 13 evaluation artifacts shows that production dynamics are often partially modeled. Reported gains also depend on evaluation fidelity. Key future directions include cross-layer coordination, telemetry-to-control abstractions, safe learning-based control, and reproducible dynamic evaluation.

Problem

• microservices
• dynamic computing environments
• adaptive management
• workload variability
• system dynamics

Innovation

• adaptive management
• microservices
• dynamic environments
• taxonomy
• learning-based control